Technology is changing how everything gets done—from how we communicate to how we shop to how we drive. Technology is also transforming how business gets done—and the implication is clear: use technology to your company’s advantage, or risk falling behind.

But the constant wave of new technologies can be overwhelming for small businesses to understand, let alone keep up with. In this three-part blog series, we discuss Intuit’s plans to make it easier for small businesses to use and get value from technology. In this first post, we talk to Ashok Srivastava, senior vice president and chief data officer at Intuit, about artificial intelligence (AI) and machine learning (ML).

Laurie: Can we start with your definition of AI and ML?

Ashok: AI is the process and science of building new systems that gather data and take action on that data. A simple example is a thermostat with a sensor that measures room temperature and transmits that information to the Internet so that it can automatically adjust temperature based on your preferences.

Machine learning depends on neural networks, which are algorithms that adapt and learn from data. Say you’re a car dealer and have a data set with age, gender, socioeconomic status, where they live and work, etc., for 1,000 people. You want to use that data to predict the propensity of different types of people to buy a car. A machine learning algorithm can model the relationships between the different characteristics and predict the propensity to buy based on this data. As new data comes in, the algorithm adapts to improve its predictions.

Taking it a step further, deep learning systems can construct multi-layered networks and process information across different dimensions for more complex predictions. For instance, you show a deep learning system thousands of pictures of rockets, dogs, skiers and whatever to teach it to differentiate images and learn to predict that a given image is more likely to be a dog than a cat.

Laurie: Where does reinforcement learning come in?

Ashok: Reinforcement learning is very interesting. I still remember the example that my professor used. Imagine you are learning how to shoot baskets [when playing basketball]. You stand in front of the hoop and toss the ball. Sometimes it goes in; sometimes it doesn’t. You don’t actually understand what you did or didn’t do to make the shot or not. You don’t say, “I need to move my hand two degrees and throw it with 10 times more force.” You readjust somehow.

Reinforcement learning tries to mimic this by trying things again and again and adjusting based on the learning algorithm. It does an internal comparison that says, “I made this adjustment, and maybe it led to my making the shot.” As it keeps trying, it learns and keeps updating its internal model of success and failure.

Small robots that navigate rooms do this. They drive around and bump into walls, chairs and coffee tables. Then, all of a sudden, they stop doing it because they are learning how to navigate their environment. They start to understand, “I won’t go that way, because I’m going to run into a wall or a piece of furniture.”

Laurie: What’s the “secret sauce” behind these technologies?

Ashok: People. People build these models to make sure they’re doing what you want them to do and that they adapt as they get new information. People also need to monitor algorithms or write other algorithms to watch the machine learning algorithms. Monitoring algorithms check that everything is happening properly with the original algorithm that you built.

Laurie: Do you have an example?

Ashok: Small businesses have to keep track of a lot of transactions, such as whether something is a travel expense or related to supplies or advertising and what’s tax deductible. It takes a lot of time for people to categorize these transactions. Intuit has built algorithms to automatically map transactions into different categories and to monitor the quality of those classifications.

Laurie: Do you have an example of recommendations in QuickBooks that use AI and ML?

Ashok: When a user types a question into the QuickBooks search box, the system provides the most relevant information back through a combination of overnight batch processing and real-time processing. This is very similar to how search engines work.

Laurie: Where do you see these technologies headed to help small businesses in the future?

Ashok: One big challenge business owners face is getting a better understanding of their finances. Intuit looks to democratize financial literacy using AI and ML to help QuickBooks users to get the insights they need more efficiently. And they’ll be able to get this info directly from QuickBooks through voice and chatbot interfaces.

Laurie: Some people fear that robots will replace people in many jobs. What do you think?

Ashok: I’m optimistic that AI and ML will level the playing field for small businesses. By providing these capabilities in the applications people already use, small businesses can take advantage of technologies that right now are mostly consumable by extremely large companies.

However, as a society, we need to make sure that we have the right avenues in place to help people to learn new skills and develop themselves and their small businesses.

Laurie: Do you have an example—one that’s not a VC-backed Silicon Valley–based startup?

Ashok: Yes. I visited a limousine company as part of a recent “follow me home,” where Intuit employees spend time observing our customers, including small businesses. The owner hadn’t considered using AI and ML to help grow his business. But as we talked, the business owner realized that he could benefit from some of the things QuickBooks already does, like auto-categorization of expenses and some of the newer things we’re working on.

Laurie: Where would you say Intuit is in this journey to empower small businesses with AI and ML capabilities?

Ashok: Intuit has invested in AI and machine learning and building technologies for small businesses and consumers for over 10 years. In the last several months, we’ve accelerated our journey to bring AI and ML directly into Intuit’s products in a seamless, easily consumable way so that small businesses can run their business more efficiently.

We also want to help them make more money and become more prosperous. We’re building recommendation systems to provide them with insights and recommendations as part of their normal course of work to help them with this. For example, when you log into QuickBooks, auto-categorization tells you immediately if it has discovered a tax-deductible expense that you missed—saving you money.

Laurie: What about help with business decisions? For instance, if I run a law firm in northern Florida, will I be able to see where I’m performing well, where I’m not, and what I need to improve performance?

Ashok: We are always looking at ways to use technology to solve our customers’ problems. Today, QuickBooks Assistant can answer questions such as, “How do my June finances compare to November?” Over time, we will evolve this to provide more in-depth information.

Laurie: As AI and ML capabilities are embedded into applications such as QuickBooks, do small business owners really need to know about the technology underneath? Most people don’t know how Alexa or Siri work; they just use them to get things done.

Ashok: Intuit is committed to embedding these capabilities into our apps. We want users to run and grow their businesses, not to become part-time technologists. The outcome should be that it’s just happening, and now I can focus on building my limo business.

Laurie: Anything else you’d like to add?

Ashok: Small businesses are the very fabric of the world economy and the U.S. economy—and Intuit uses AI and ML to help small businesses take advantage of the same capabilities that very large companies do. That is going to generate tremendous societal benefit.